private void UpdateLayer(int PWhichLayer, bool PForward, AutoencoderWeights PWeights)
        {
            int beginlayer = PWhichLayer - 1;

            if (PForward)
            {
                Utility.WithinBounds("Cannot update this layer!!!", PWhichLayer, 1, numlayers);
            }
            else
            {
                Utility.WithinBounds("Cannot update this layer!!!", PWhichLayer, 0, numlayers - 1);
                beginlayer = PWhichLayer + 1;
            }
            RBMLayer thislayer     = layers[PWhichLayer];
            RBMLayer previouslayer = layers[beginlayer];
            double   input         = 0;

            double[] states = previouslayer.GetStates();
            for (int i = 0; i < thislayer.Count; i++)
            {
                for (int j = 0; j < previouslayer.Count; j++)
                {
                    if (!PForward)
                    {
                        input += PWeights.GetWeightSet(beginlayer - 1).GetWeight(i, j) * states[j];
                    }
                    else
                    {
                        input += PWeights.GetWeightSet(beginlayer).GetWeight(j, i) * states[j];
                    }
                }
                thislayer.SetState(i, input);
                input = 0;
            }
        }
Exemple #2
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        public object Clone()
        {
            AutoencoderWeights retval = new AutoencoderWeights();

            retval.numweightsets = numweightsets;
            retval.weights       = new RBMWeightSet[numweightsets];
            for (int i = 0; i < numweightsets; i++)
            {
                retval.weights[i] = (RBMWeightSet)weights[i].Clone();
            }
            return(retval);
        }
 internal Autoencoder(List <RBMLayer> PLayers, AutoencoderLearningRate PTrainingInfo
                      , IWeightInitializer PWInitializer)
 {
     numlayers          = PLayers.Count;
     layers             = PLayers.ToArray();
     learnrate          = PTrainingInfo;
     recognitionweights = new AutoencoderWeights(numlayers, layers, PWInitializer);
     generativeweights  = new AutoencoderWeights(numlayers, layers, PWInitializer);
     errorobservers     = new List <IErrorObserver>();
     InitBiases(PWInitializer);
     InitTrainingData();
 }
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        ////////////////////////////////////////////////////////////////////////////////////////////////////
        /// <summary>   Pre training complete. </summary>
        ///
        /// <exception cref="Exception">    Thrown when an exception error condition occurs. </exception>
        ////////////////////////////////////////////////////////////////////////////////////////////////////

        public void PreTrainingComplete()
        {
            if (!pretraining)
            {
                throw new AutoEncoderException("You can only call PreTrainingComplete() once!");
            }
            pretraining = false;
            for (int i = 0; i < numlayers; i++)
            {
                generativeweights = (AutoencoderWeights)recognitionweights.Clone();
            }
        }
 public void PreTrainingComplete()
 {
     if (!pretraining)
     {
         throw new Exception("You have already called PreTrainingComplete()!"
                             + " Don't call it twice!");
     }
     pretraining = false;
     for (int i = 0; i < numlayers; i++)
     {
         generativeweights = (AutoencoderWeights)recognitionweights.Clone();
     }
 }
        ////////////////////////////////////////////////////////////////////////////////////////////////////
        /// <summary>   Creates a new object that is a copy of the current instance. </summary>
        ///
        /// <returns>   A new object that is a copy of this instance. </returns>
        ///
        /// <seealso cref="M:System.ICloneable.Clone()"/>
        ////////////////////////////////////////////////////////////////////////////////////////////////////

        public object Clone()
        {
            AutoencoderWeights retval = new AutoencoderWeights
            {
                numweightsets = numweightsets,
                weights       = new RestrictedBoltzmannMachineWeightSet[numweightsets]
            };

            for (int i = 0; i < numweightsets; i++)
            {
                retval.weights[i] = (RestrictedBoltzmannMachineWeightSet)weights[i].Clone();
            }
            return(retval);
        }